arXiv:2607. 10317v1 Announce Type: cross Abstract: Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
arXiv:2607. 13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams.
arXiv:2608. 15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions.
arXiv:2606. 17555v1 Announce Type: cross Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements.
arXiv:2606. 08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers.
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.